删除不需要的线条并平滑验证码文本 - opencv4 - python

Question

我尝试使用 opencv 从验证码图像中获取文本图像来构建我的训练数据集。我提取的验证码不是那么平滑和优化。有人可以帮我优化并建议最好的方法来消除不需要的噪音，并获得平滑的验证码文本。 现在，我首先添加一些填充，对其进行阈值处理并找到轮廓。
问题：1.使用border_replicate添加填充可能会增加一些噪音
2.thresholding不流畅和完美
原图：
阈值图像：
轮廓图像：

代码：

import os
import os.path
import cv2
import glob
import imutils
import matplotlib.pyplot as plt

CAPTCHA_IMAGE_FOLDER = "generated_captcha_images"
OUTPUT_FOLDER = "extracted_letter_images"


# Get a list of all the captcha images we need to process
captcha_image_files = glob.glob(os.path.join(CAPTCHA_IMAGE_FOLDER, "*"))
counts = {}

# loop over the image paths
for (i, captcha_image_file) in enumerate(captcha_image_files):
    print("[INFO] processing image {}/{}".format(i + 1, len(captcha_image_files)))

    # Since the filename contains the captcha text (i.e. "2A2X.png" has the text "2A2X"),
    # grab the base filename as the text
    filename = os.path.basename(captcha_image_file)
    captcha_correct_text = os.path.splitext(filename)[0]

    # Load the image and convert it to grayscale
    image = cv2.imread(captcha_image_file)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Add some extra padding around the image
    gray = cv2.copyMakeBorder(gray, 8, 8, 8, 8, cv2.BORDER_REPLICATE)
    # threshold the image (convert it to pure black and white)

    thresh = cv2.threshold(
        gray, 36, 255, cv2.THRESH_BINARY_INV)[1]

    # find the contours (continuous blobs of pixels) the image
    contours = cv2.findContours(
        thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Hack for compatibility with different OpenCV versions
    contours = contours[1] if imutils.is_cv3() else contours[0]
    letter_image_regions = []

    for contour in contours:
        # Get the rectangle that contains the contour
        (x, y, w, h) = cv2.boundingRect(contour)
        
        if w >= 23:
            half_width = int(w / 3)
            letter_image_regions.append((x, y, half_width, h))
            letter_image_regions.append((x + half_width, y, half_width, h))
            letter_image_regions.append((x + 2*half_width, y, half_width, h))
        elif w >= 13:
            # This contour is too wide to be a single letter!
            # Split it in half into two letter regions!
            half_width = int(w / 2)
            letter_image_regions.append((x, y, half_width, h))
            letter_image_regions.append((x + half_width, y, half_width, h))
        else:
            # This is a normal letter by itself
            letter_image_regions.append((x, y, w, h))

    if len(letter_image_regions) != 5:
        print("----NOT PROCESSED:  "+captcha_correct_text+"----------")
        continue
    # else:
        # os.remove(captcha_image_file)
        # Sort the detected letter images based on the x coordinate to make sure
        # we are processing them from left-to-right so we match the right image
        # with the right letter
    letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])

    # Save out each letter as a single image
    for letter_bounding_box, letter_text in zip(letter_image_regions, captcha_correct_text):
        # Grab the coordinates of the letter in the image
        x, y, w, h = letter_bounding_box

        # Extract the letter from the original image with a 2-pixel margin around the edge
        letter_image = gray[y - 2:y + h + 2, x - 2:x + w + 2]
        # letter_image = gray[y:y+h, x:x+w]
        # Get the folder to save the image in
        save_path = os.path.join(OUTPUT_FOLDER, letter_text)

        # if the output directory does not exist, create it
        if not os.path.exists(save_path):
            os.makedirs(save_path)

        # write the letter image to a file
        count = counts.get(letter_text, 1)
        p = os.path.join(save_path, "{}.png".format(str(count).zfill(6)))
        cv2.imwrite(p, letter_image)

        # increment the count for the current key
        counts[letter_text] = count + 1

Answer 1

我想，只需使用灰度滤镜和腐蚀/膨胀就足以获得所需的验证码图像。有人可以告诉我如何将其应用于示例原始图像。